情报科学 ›› 2023, Vol. 41 ›› Issue (3): 45-56.
重大突发公共卫生事件中社交媒体信息过载的前因后果模型研究
- 【目的/意义】重大突发公共卫生事件中信息疫情现象危害社交媒体用户的身心健康，信息过载是全媒体时
代信息疫情的主要表现形式。探究重大突发公共卫生事件情境下社交媒体信息过载的成因和影响，能为信息疫情
的应对提供理论支持和实践参考。【方法/过程】通过半结构化访谈的方式获取原始资料，运用扎根理论的质性分析
方法，对访谈文本进行编码处理，并借鉴压力源-应变-结果理论，构建重大突发公共卫生事件中社交媒体信息过载
前因后果理论模型。【结果/结论】研究结果表明，重大突发公共卫生事件中社交媒体信息过载的前因包括用户因
素、信息因素、技术因素和环境因素，信息过载直接影响社交媒体用户的认知反应和情绪反应，间接影响社交媒体
用户的应对行为。【创新/局限】应用质性分析方法从多维度系统地探究重大突发公共卫生事件中社交媒体信息过
载的驱动因素和影响机制，有力补充信息过载领域现有研究。后续可通过实证方法、大样本数据验证模型的科
学性。
- 【Purpose/significance】Information epidemic in major public health emergencies endangers the physical and mental health
of social media users. Information overload is the main manifestation of information epidemic in the all-media era. To explore the ante? cedents and consequences of social media information overload in the context of major public health emergencies can provide theoreti? cal support and practical reference for information epidemic response【. Method/process】The original data were obtained through semi?structured interview, and the interview text was coded by using the qualitative analysis method of grounded theory, and the theoretical model of antecedents and consequences of social media information overload in major public health emergencies was constructed based on the stressor response outcome theory【. Result/conclusion】The results show that the antecedents of social media information overload in major public health emergencies include user factors, information factors, technical factors and environmental factors,and information overload directly affects the affective response and coping behavior of social media users【. Innovation/limitation】Qualita? tive analysis is applied to systematically explore the driving factors and influencing mechanism of social media information overload in major public health emergencies from multiple dimensions, which effectively complements the existing research in the field of informa? tion overload. Subsequently, we can verify the scientificity of the model through empirical methods and large sample data.
